CRESCENT: Collision-Free Highly Constrained Trajectory Optimization for Driving on the Moon (I)

Abstract

Rovers have been a mainstay of planetary exploration missions, significantly expanding our knowledge in planetary science. However, past rover missions have involved significant human supervision to oversee rover operations, a state-of-practice that scales poorly for the next generation of missions. In this work, we present the development of Constrained Roving Exploration via Safe Collision-free and Environment-aware Trajectory optimization (CRESCENT), a motion planning algorithm developed for the upcoming multiagent Cooperative Autonomous Distributed Robotic Exploration (CADRE) Lunar rover mission. CRESCENT was designed to safely drive a miniature rover platform in a highly cluttered unmapped Lunar environment, executing complex motion directives from CADRE’s team-level autonomy while meeting far stricter dynamical and temporal constraints than existing onboard planetary rover planning algorithms are capable of satisfying. Our hierarchical approach formulates an efficient numerical trajectory optimization-based motion planning algorithm that makes use of nonlinear optimization to solve the planning problem in real time. We demonstrate the efficiency of our proposed approach through extensive simulations and hardware testing in a representative Lunar environment. Following CADRE’s upcoming deployment on the Lunar surface, CRESCENT will be the first nonlinear optimization-based trajectory optimization approach used on another celestial body.